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Diagnostics
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Advances in Diagnosis and Management of Neuropsychiatric Disorders
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Advances in Diagnosis and Management of Neuropsychiatric Disorders

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Pathology and Molecular Diagnostics".

Deadline for manuscript submissions: closed (28 February 2025) | Viewed by 7278

Special Issue Editor

Dr. Jesús Ramírez-Bermúdez

E-Mail Website
Guest Editor
National Institute of Neurology and Neurosurgery, Mexico City, Mexico
Interests: neuropsychiatry; mania; MRI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the ongoing changes in social pressure and our contemporary lifestyles, the incidence of neuropsychiatric disorders (including autism, depression, mania, etc.) is on the rise, becoming a global health issue. Due to these disorders’ complex and diverse symptoms and the fact that many diseases overlap, there are many difficulties in diagnosis and a high risk of misdiagnosis and missed diagnosis. This Special Issue aims to share the latest advancements in the diagnosis and management of neuropsychiatric disorders, explore pathogenic mechanisms, focus on the latest diagnostic technologies, and help patients receive timely and correct treatment. The scope includes but is not limited to the following:

  1. Pathogenesis of neuropsychiatric disorders;
  2. Imaging diagnosis of neuropsychiatric disorders;
  3. Biomarkers in the diagnosis and management of neuropsychiatric disorders;
  4. Prognostic evaluation of neuropsychiatric disorder treatment.

We welcome your contributions.

Dr. Jesús Ramírez-Bermúdez
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • neuropsychiatric disorders
  • autism
  • mania
  • depression
  • imaging
  • pathogenesis

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

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Research

13 pages, 1511 KiB  
Article
The Clinical Significance of Abnormal Electroencephalography (EEG) Patterns in Patients with Neuropsychiatric Disorders Due to Anti-NMDA Receptor Encephalitis: A Comparative Study
by Alvaro Moreno-Avellán, Arely Juarez-Jaramillo, Maria del Carmen Fernandez Gonzalez-Aragon, Gerardo Quiñones-Pesqueira, Luz Maria Pineda-Centeno, Mariana Espinola-Nadurille, Victoria Martinez-Angeles, Francisco Martinez-Carrillo, Veronica Rivas-Alonso, Daniel San-Juan, Jose Flores-Rivera and Jesus Ramirez-Bermudez
Diagnostics 2025, 15(9), 1131; https://doi.org/10.3390/diagnostics15091131 - 29 Apr 2025
Abstract
Background: Anti-NMDA receptor encephalitis is an autoimmune disease characterized by severe neuropsychiatric disturbances, often misdiagnosed as a primary psychiatric disorder. Early diagnosis is crucial, as delayed immunotherapy is associated with worse outcomes. Electroencephalography (EEG) is a widely available tool for detecting abnormalities that [...] Read more.
Background: Anti-NMDA receptor encephalitis is an autoimmune disease characterized by severe neuropsychiatric disturbances, often misdiagnosed as a primary psychiatric disorder. Early diagnosis is crucial, as delayed immunotherapy is associated with worse outcomes. Electroencephalography (EEG) is a widely available tool for detecting abnormalities that may aid in early detection of cases that should undergo a thorough approach. Although EEG has high sensitivity, its specificity remains a challenge. Methods: This case-control study was carried out in the National Institute of Neurology and Neurosurgery of Mexico and included 241 patients with acute or subacute neuropsychiatric disturbances, raising the suspicion of autoimmune encephalitis and leading to the determination of NMDA receptor antibodies in the cerebrospinal fluid (CSF). EEG patterns were analyzed to determine the frequency of abnormal findings and their diagnostic value. Results: 140 patients were diagnosed as having definite anti-NMDA receptor encephalitis, whereas 101 had a negative determination of NMDA receptor antibodies. Psychosis was very frequent in both groups. However, severe cognitive dysfunction and catatonia were significantly more frequent in anti-NMDA receptor encephalitis patients. EEG abnormalities were significantly more frequent in patients with anti-NMDA receptor encephalitis patients (87.2% vs. 61.2%, p < 0.001). Diffuse slowing (75.7% vs. 46.6%, p < 0.001) and the extreme delta brush pattern (8.8% vs. 0%, OR = 20.6, p = 0.002) were significantly associated with anti-NMDA receptor encephalitis. Logistic regression analysis confirmed that an abnormal EEG remained strongly associated with anti-NMDA receptor encephalitis after adjusting for confounders. Conclusions: EEG abnormalities, particularly diffuse slowing and the extreme delta brush pattern, provide important diagnostic clues in patients with a clinical suspicion of anti-NMDA receptor encephalitis. While EEG has high sensitivity, its specificity is enhanced by recognizing distinct patterns. These findings support the integration of EEG into diagnostic algorithms to guide early detection and management of autoimmune encephalitis. Full article
(This article belongs to the Special Issue Advances in Diagnosis and Management of Neuropsychiatric Disorders)
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17 pages, 1053 KiB  
Article
Dangerousness Index in Forensic Psychiatric Examination: A Tool for Aiding Medical Decision Regarding the Risk of Antisocial Acts
by Daniela Margareta Varga, Florica Voiță-Mekeres, Gabriel Mihai Mekeres, Călin David Buzlea, Lavinia Davidescu and Camelia Liana Buhas
Diagnostics 2025, 15(8), 1004; https://doi.org/10.3390/diagnostics15081004 - 15 Apr 2025
Viewed by 256
Abstract
Background and Objectives: The assessment of dangerousness and risk of recidivism are crucial aspects of forensic psychiatric evaluations, influencing therapeutic and security measures. This study aimed to develop and validate a new tool, the Dangerousness Index in Forensic Psychiatry (IPPML), following a [...] Read more.
Background and Objectives: The assessment of dangerousness and risk of recidivism are crucial aspects of forensic psychiatric evaluations, influencing therapeutic and security measures. This study aimed to develop and validate a new tool, the Dangerousness Index in Forensic Psychiatry (IPPML), following a psychometric scale construction methodology. Materials and Methods: The sample consisted of 261 participants (157 males, 104 females) aged 19–75 years, divided into an experimental group (n = 126) with a history of forensic psychiatric examination and a control group (n = 135) diagnosed with schizophrenia. Results: Exploratory factor analysis revealed two factors, Performance and Social, explaining 45.55% of the data variance. The IPPML demonstrated adequate internal consistency (α = 0.881) for the entire sample, with Factor 1 showing strong consistency (α = 0.896) and Factor 2 exhibiting acceptable consistency (α = 0.628). Reliability ranged from 89.6% to 62.8% when administered to participants with psychoses undergoing forensic psychiatric evaluation, decreasing to 42.5% for legally evaluated patients and increasing from 58.7% to 84.3% for participants with schizophrenia without forensic psychiatric evaluation. Discriminant validity analysis indicated higher psychiatric dangerousness with forensic implications in males. Conclusions: The IPPML shows promise as a tool for assessing dangerousness in forensic psychiatry and aiding medical decision-making regarding the risk of antisocial and potentially harmful acts. Full article
(This article belongs to the Special Issue Advances in Diagnosis and Management of Neuropsychiatric Disorders)
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13 pages, 721 KiB  
Article
Time Perception Test in IntelliCage System for Preclinical Study: Linking Depression and Serotonergic Modulation
by Olga Sysoeva, Rauf Akhmirov, Maria Zaichenko, Ivan Lazarenko, Anastasiya Rebik, Nadezhda Broshevitskaja, Inna Midzyanovskaya and Kirill Smirnov
Diagnostics 2025, 15(2), 151; https://doi.org/10.3390/diagnostics15020151 - 10 Jan 2025
Viewed by 691
Abstract
Background/Objectives:: The link between serotonergic modulation and depression is under debate; however, serotonin reuptake inhibitors (SRIs) are still the first-choice medicine in this condition. Disturbances in time perception are also reported in depression with one of the behavioral schedules used to study interval [...] Read more.
Background/Objectives:: The link between serotonergic modulation and depression is under debate; however, serotonin reuptake inhibitors (SRIs) are still the first-choice medicine in this condition. Disturbances in time perception are also reported in depression with one of the behavioral schedules used to study interval timing, differential-reinforcement-learning-of-low-rate, having been shown to have high predictive validity for an antidepressant effect. Here, we introduce an IntelliCage research protocol of an interval bisection task that allows more ecologically valid and less time-consuming rodent examination and provides an example of its use to confirm the previously reported acute effect of an SRI, clomipramine, on interval timing (increase in bisection point, D50). Methods: Wistar male rats (n = 25, five groups of 5–8) were trained in the IntelliCage to discriminate between short (1 s) and long (4 s) LED light stimuli by nose poking at the corresponding (left/right) side of the IntelliCage chamber to obtain a drink. When 80% of correct responses were reached, the intermediate durations of 1.7, 2.5, and 3.3 s were introduced. The number of left/right choices for each stimulus and interval timing parameters (bisection point, D50, and timing precision), derived from them, were compared after saline and clomipramine (7 mg/kg, i.p) intraperitoneal administration. Results: Rats successfully learned the task within about a week of training. The slightly increased D50 after clomipramine confirmed previous studies. Conclusions: The introduced protocol has potential to be applicable to preclinical research on depression and potentially other psychopathology, where time perception can be disturbed. Full article
(This article belongs to the Special Issue Advances in Diagnosis and Management of Neuropsychiatric Disorders)
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14 pages, 2824 KiB  
Article
Moderate Alcohol Consumption Increases the Risk of Clinical Relapse in Male Depressed Patients Treated with Serotonin-Norepinephrine Reuptake Inhibitors
by Mădălina Iuliana Mușat, Felicia Militaru, Victor Gheorman, Ion Udriștoiu, Smaranda Ioana Mitran and Bogdan Cătălin
Diagnostics 2024, 14(11), 1140; https://doi.org/10.3390/diagnostics14111140 - 30 May 2024
Cited by 5 | Viewed by 1330
Abstract
Background: While depression can be associated with multiple comorbidities, the association between depression and liver injury significantly increases the mortality risk. The aim of this study was to evaluate if moderate alcohol intake affects the rate of clinical relapses in patients treated with [...] Read more.
Background: While depression can be associated with multiple comorbidities, the association between depression and liver injury significantly increases the mortality risk. The aim of this study was to evaluate if moderate alcohol intake affects the rate of clinical relapses in patients treated with antidepressants as monotherapy. Methods: We assessed, over a period of 30 months, the clinical records of 254 patients with depressive disorder, of either gender, without additional pathologies, receiving monotherapy treatment with antidepressants. Thirty-three patients with alcohol abuse, alcoholism or significant cognitive impairment were excluded. The medical and psychiatric history, medication and liver enzyme values were collected and analyzed. Results: Out of the 221 patients who met the inclusion criteria, 78 experienced relapses of depression. The rate of relapse did not correlate with the levels of liver enzymes. Alcohol consumption, as objectified based on GGT levels and the AST/ALT ratio, suggested that men had higher alcohol intake compared to women. Patients treated with serotonin-norepinephrine reuptake inhibitors (SNRIs) with elevated AST levels were approximately 9 times more likely to relapse, while the ones with elevated GGT had a 5.34 times higher risk. While GGT levels remained a marker for relapse in men with elevated GGT, ALT and not AST proved to be a better risk indicator for relapses in male patients. Conclusion: The use of SNRIs in depressed male patients with moderate alcohol intake should be carefully considered, as they might be susceptible to higher risks of relapse compared to alternative antidepressant therapies. Full article
(This article belongs to the Special Issue Advances in Diagnosis and Management of Neuropsychiatric Disorders)
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11 pages, 1376 KiB  
Article
Identifying Autism Gaze Patterns in Five-Second Data Records
by Pedro Lencastre, Maryam Lotfigolian and Pedro G. Lind
Diagnostics 2024, 14(10), 1047; https://doi.org/10.3390/diagnostics14101047 - 18 May 2024
Cited by 1 | Viewed by 1918
Abstract
One of the most challenging problems when diagnosing autism spectrum disorder (ASD) is the need for long sets of data. Collecting data during such long periods is challenging, particularly when dealing with children. This challenge motivates the investigation of possible classifiers of ASD [...] Read more.
One of the most challenging problems when diagnosing autism spectrum disorder (ASD) is the need for long sets of data. Collecting data during such long periods is challenging, particularly when dealing with children. This challenge motivates the investigation of possible classifiers of ASD that do not need such long data sets. In this paper, we use eye-tracking data sets covering only 5 s and introduce one metric able to distinguish between ASD and typically developed (TD) gaze patterns based on such short time-series and compare it with two benchmarks, one using the traditional eye-tracking metrics and one state-of-the-art AI classifier. Although the data can only track possible disorders in visual attention and our approach is not a substitute to medical diagnosis, we find that our newly introduced metric can achieve an accuracy of 93% in classifying eye gaze trajectories from children with ASD surpassing both benchmarks while needing fewer data. The classification accuracy of our method, using a 5 s data series, performs better than the standard metrics in eye-tracking and is at the level of the best AI benchmarks, even when these are trained with longer time series. We also discuss the advantages and limitations of our method in comparison with the state of the art: besides needing a low amount of data, this method is a simple, understandable, and straightforward criterion to apply, which often contrasts with “black box” AI methods. Full article
(This article belongs to the Special Issue Advances in Diagnosis and Management of Neuropsychiatric Disorders)
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14 pages, 2175 KiB  
Article
AI-Enhanced Predictive Modeling for Identifying Depression and Delirium in Cardiovascular Patients Scheduled for Cardiac Surgery
by Karina Nowakowska, Antonis Sakellarios, Jakub Kaźmierski, Dimitrios I. Fotiadis and Vasileios C. Pezoulas
Diagnostics 2024, 14(1), 67; https://doi.org/10.3390/diagnostics14010067 - 27 Dec 2023
Cited by 7 | Viewed by 2408
Abstract
Several studies have demonstrated a critical association between cardiovascular disease (CVD) and mental health, revealing that approximately one-third of individuals with CVD also experience depression. This comorbidity significantly increases the risk of cardiac complications and mortality, a risk that persists regardless of traditional [...] Read more.
Several studies have demonstrated a critical association between cardiovascular disease (CVD) and mental health, revealing that approximately one-third of individuals with CVD also experience depression. This comorbidity significantly increases the risk of cardiac complications and mortality, a risk that persists regardless of traditional factors. Addressing this issue, our study pioneers a straightforward, explainable, and data-driven pipeline for predicting depression in CVD patients. Methods: Our study was conducted at a cardiac surgical intensive care unit. A total of 224 participants who were scheduled for elective coronary artery bypass graft surgery (CABG) were enrolled in the study. Prior to surgery, each patient underwent psychiatric evaluation to identify major depressive disorder (MDD) based on the DSM-5 criteria. An advanced data curation workflow was applied to eliminate outliers and inconsistencies and improve data quality. An explainable AI-empowered pipeline was developed, where sophisticated machine learning techniques, including the AdaBoost, random forest, and XGBoost algorithms, were trained and tested on the curated data based on a stratified cross-validation approach. Results: Our findings identified a significant correlation between the biomarker “sRAGE” and depression (r = 0.32, p = 0.038). Among the applied models, the random forest classifier demonstrated superior accuracy in predicting depression, with notable scores in accuracy (0.62), sensitivity (0.71), specificity (0.53), and area under the curve (0.67). Conclusions: This study provides compelling evidence that depression in CVD patients, particularly those with elevated “sRAGE” levels, can be predicted with a 62% accuracy rate. Our AI-driven approach offers a promising way for early identification and intervention, potentially revolutionizing care strategies in this vulnerable population. Full article
(This article belongs to the Special Issue Advances in Diagnosis and Management of Neuropsychiatric Disorders)
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